




'''simple docstring'''


from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

UpperCAmelCase_					=   logging.get_logger(__name__)


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				List[str]					,       a_						:				str      ):
  return [
      int(1000 * (box[0] / width)      ),
      int(1000 * (box[1] / height)      ),
      int(1000 * (box[2] / width)      ),
      int(1000 * (box[3] / height)      ),
  ]


def     SCREAMING_SNAKE_CASE							(   a_						:				np.ndarray					,       a_						:				Optional[str]					,       a_						:				Optional[str]      ):
  __a								=       to_pil_image(a_      )
  __a						,							__a								=       pil_image.size
  __a								=       pytesseract.image_to_data(a_					,       lang=a_					,       output_type='dict'					,       config=a_      )
  __a						,							__a						,							__a						,							__a						,							__a								=       data['text'], data['left'], data['top'], data['width'], data['height']

  # filter empty words and corresponding coordinates
  __a								=       [idx for idx, word in enumerate(a_      ) if not word.strip()]
  __a								=       [word for idx, word in enumerate(a_      ) if idx not in irrelevant_indices]
  __a								=       [coord for idx, coord in enumerate(a_      ) if idx not in irrelevant_indices]
  __a								=       [coord for idx, coord in enumerate(a_      ) if idx not in irrelevant_indices]
  __a								=       [coord for idx, coord in enumerate(a_      ) if idx not in irrelevant_indices]
  __a								=       [coord for idx, coord in enumerate(a_      ) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  __a								=       []
  for x, y, w, h in zip(a_					,       a_					,       a_					,       a_      ):
    __a								=       [x, y, x + w, y + h]
    actual_boxes.append(a_      )

  # finally, normalize the bounding boxes
  __a								=       []
  for box in actual_boxes:
    normalized_boxes.append(normalize_box(a_					,       a_					,       a_      )      )

  assert len(a_      ) == len(a_      ), "Not as many words as there are bounding boxes"

  return words, normalized_boxes





class  __lowercase    (	__magic_name__			):
   _a    						=   ["""pixel_values"""]







   def __init__(						self   ,				UpperCamelCase = True   ,				UpperCamelCase = None   ,				UpperCamelCase = PILImageResampling.BILINEAR   ,				UpperCamelCase = True   ,				UpperCamelCase = 1 / 255   ,				UpperCamelCase = True   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = True   ,				UpperCamelCase = None   ,				UpperCamelCase = ""   ,				**UpperCamelCase   ,				)					->     None:
     super().__init__(**UpperCamelCase   )
     __a								=       size if size is not None else {'height': 224, 'width': 224}
     __a								=       get_size_dict(UpperCamelCase   )

     __a								=       do_resize
     __a								=       size
     __a								=       resample
     __a								=       do_rescale
     __a								=       rescale_value
     __a								=       do_normalize
     __a								=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a								=       image_std if image_std is not None else IMAGENET_STANDARD_STD
     __a								=       apply_ocr
     __a								=       ocr_lang
     __a								=       tesseract_config







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase = PILImageResampling.BILINEAR   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     np.ndarray:
     __a								=       get_size_dict(UpperCamelCase   )
     if "height" not in size or "width" not in size:
       raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"   )
     __a								=       (size['height'], size['width'])
     return resize(UpperCamelCase   ,				size=UpperCamelCase   ,				resample=UpperCamelCase   ,				data_format=UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     np.ndarray:
     return rescale(UpperCamelCase   ,				scale=UpperCamelCase   ,				data_format=UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     np.ndarray:
     return normalize(UpperCamelCase   ,				mean=UpperCamelCase   ,				std=UpperCamelCase   ,				data_format=UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase=None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = ChannelDimension.FIRST   ,				**UpperCamelCase   ,				)					->     PIL.Image.Image:
     __a								=       do_resize if do_resize is not None else self.do_resize
     __a								=       size if size is not None else self.size
     __a								=       get_size_dict(UpperCamelCase   )
     __a								=       resample if resample is not None else self.resample
     __a								=       do_rescale if do_rescale is not None else self.do_rescale
     __a								=       rescale_factor if rescale_factor is not None else self.rescale_factor
     __a								=       do_normalize if do_normalize is not None else self.do_normalize
     __a								=       image_mean if image_mean is not None else self.image_mean
     __a								=       image_std if image_std is not None else self.image_std
     __a								=       apply_ocr if apply_ocr is not None else self.apply_ocr
     __a								=       ocr_lang if ocr_lang is not None else self.ocr_lang
     __a								=       tesseract_config if tesseract_config is not None else self.tesseract_config

     __a								=       make_list_of_images(UpperCamelCase   )

     if not valid_images(UpperCamelCase   ):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'   )

     if do_resize and size is None:
       raise ValueError('Size must be specified if do_resize is True.'   )

     if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'   )

     # All transformations expect numpy arrays.
     __a								=       [to_numpy_array(UpperCamelCase   ) for image in images]

     # Tesseract OCR to get words + normalized bounding boxes
     if apply_ocr:
       requires_backends(self   ,				'pytesseract'   )
       __a								=       []
       __a								=       []
       for image in images:
         __a						,							__a								=       apply_tesseract(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
         words_batch.append(UpperCamelCase   )
         boxes_batch.append(UpperCamelCase   )

     if do_resize:
       __a								=       [self.resize(image=UpperCamelCase   ,				size=UpperCamelCase   ,				resample=UpperCamelCase   ) for image in images]

     if do_rescale:
       __a								=       [self.rescale(image=UpperCamelCase   ,				scale=UpperCamelCase   ) for image in images]

     if do_normalize:
       __a								=       [self.normalize(image=UpperCamelCase   ,				mean=UpperCamelCase   ,				std=UpperCamelCase   ) for image in images]

     __a								=       [to_channel_dimension_format(UpperCamelCase   ,				UpperCamelCase   ) for image in images]

     __a								=       BatchFeature(data={'pixel_values': images}   ,				tensor_type=UpperCamelCase   )

     if apply_ocr:
       __a								=       words_batch
       __a								=       boxes_batch
     return data









'''simple docstring'''


import math


def     SCREAMING_SNAKE_CASE							(   a_						:				float					,       a_						:				float      ):

  if initial_intensity < 0:
    raise ValueError('The value of intensity cannot be negative'      )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 360:
    raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'      )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(a_      )      ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



